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Malware is widely regarded as one of the most severe security threats to modern technologies. Detecting malware in the Internet of Things (IoT) infrastructures is a critical and complicated task. The complexity of this task increases with the recent growth of malware variants targeting different IoT CPU architectures since the new malware variants often use anti-forensic techniques to avoid detection and investigation. There-fore, we cannot utilize the traditional machine learning (ML) techniques that require domain knowledge and sophisticated feature engineering in detecting the unseen mal ware variants. Re-cent deep learning approaches have performed well on mal ware analysis and detection while using minimum feature engineering requirements. In this paper, we propose BERTDeep- Ware, a real-time cross-architecture malware detection solution tailored for IoT systems. BERTDeep- Ware analyzes the …